Overview

Dataset statistics

Number of variables24
Number of observations30000
Missing cells0
Missing cells (%)0.0%
Duplicate rows35
Duplicate rows (%)0.1%
Total size in memory6.7 MiB
Average record size in memory235.2 B

Variable types

Numeric21
Categorical3

Alerts

Dataset has 35 (0.1%) duplicate rowsDuplicates
Repayment_September is highly overall correlated with Repayment_August and 2 other fieldsHigh correlation
Repayment_August is highly overall correlated with Repayment_September and 7 other fieldsHigh correlation
Repayment_July is highly overall correlated with Repayment_September and 9 other fieldsHigh correlation
Repayment_June is highly overall correlated with Repayment_September and 10 other fieldsHigh correlation
Repayment_May is highly overall correlated with Repayment_August and 8 other fieldsHigh correlation
Repayment_April is highly overall correlated with Repayment_August and 8 other fieldsHigh correlation
Sep_Bill is highly overall correlated with Repayment_August and 8 other fieldsHigh correlation
Aug_Bill is highly overall correlated with Repayment_August and 10 other fieldsHigh correlation
July_Bill is highly overall correlated with Repayment_August and 11 other fieldsHigh correlation
June_Bill is highly overall correlated with Repayment_July and 13 other fieldsHigh correlation
May_Bill is highly overall correlated with Repayment_July and 13 other fieldsHigh correlation
Apr_Bill is highly overall correlated with Repayment_June and 11 other fieldsHigh correlation
Pay_Sep is highly overall correlated with Sep_Bill and 5 other fieldsHigh correlation
Pay_Aug is highly overall correlated with July_Bill and 5 other fieldsHigh correlation
Pay_July is highly overall correlated with June_Bill and 7 other fieldsHigh correlation
Pay_June is highly overall correlated with June_Bill and 6 other fieldsHigh correlation
Pay_May is highly overall correlated with June_Bill and 5 other fieldsHigh correlation
Pay_April is highly overall correlated with May_Bill and 4 other fieldsHigh correlation
Pay_Aug is highly skewed (γ1 = 30.45381745)Skewed
Repayment_September has 14737 (49.1%) zerosZeros
Repayment_August has 15730 (52.4%) zerosZeros
Repayment_July has 15764 (52.5%) zerosZeros
Repayment_June has 16455 (54.9%) zerosZeros
Repayment_May has 16947 (56.5%) zerosZeros
Repayment_April has 16286 (54.3%) zerosZeros
Sep_Bill has 2008 (6.7%) zerosZeros
Aug_Bill has 2506 (8.4%) zerosZeros
July_Bill has 2870 (9.6%) zerosZeros
June_Bill has 3195 (10.7%) zerosZeros
May_Bill has 3506 (11.7%) zerosZeros
Apr_Bill has 4020 (13.4%) zerosZeros
Pay_Sep has 5249 (17.5%) zerosZeros
Pay_Aug has 5396 (18.0%) zerosZeros
Pay_July has 5968 (19.9%) zerosZeros
Pay_June has 6408 (21.4%) zerosZeros
Pay_May has 6703 (22.3%) zerosZeros
Pay_April has 7173 (23.9%) zerosZeros

Reproduction

Analysis started2023-05-17 03:59:38.619199
Analysis finished2023-05-17 04:02:08.909623
Duration2 minutes and 30.29 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Limit_bal
Real number (ℝ)

Distinct81
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167484.32
Minimum10000
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-05-17T04:02:09.097720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median140000
Q3240000
95-th percentile430000
Maximum1000000
Range990000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation129747.66
Coefficient of variation (CV)0.77468541
Kurtosis0.5362629
Mean167484.32
Median Absolute Deviation (MAD)90000
Skewness0.99286696
Sum5.0245297 × 109
Variance1.6834456 × 1010
MonotonicityNot monotonic
2023-05-17T04:02:09.453711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 3365
 
11.2%
20000 1976
 
6.6%
30000 1610
 
5.4%
80000 1567
 
5.2%
200000 1528
 
5.1%
150000 1110
 
3.7%
100000 1048
 
3.5%
180000 995
 
3.3%
360000 881
 
2.9%
60000 825
 
2.8%
Other values (71) 15095
50.3%
ValueCountFrequency (%)
10000 493
 
1.6%
16000 2
 
< 0.1%
20000 1976
6.6%
30000 1610
5.4%
40000 230
 
0.8%
50000 3365
11.2%
60000 825
 
2.8%
70000 731
 
2.4%
80000 1567
5.2%
90000 651
 
2.2%
ValueCountFrequency (%)
1000000 1
 
< 0.1%
800000 2
 
< 0.1%
780000 2
 
< 0.1%
760000 1
 
< 0.1%
750000 4
< 0.1%
740000 2
 
< 0.1%
730000 2
 
< 0.1%
720000 3
 
< 0.1%
710000 6
< 0.1%
700000 8
< 0.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
18112 
1
11888 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 18112
60.4%
1 11888
39.6%

Length

2023-05-17T04:02:09.750247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-17T04:02:10.005729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 18112
60.4%
1 11888
39.6%

Most occurring characters

ValueCountFrequency (%)
2 18112
60.4%
1 11888
39.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 18112
60.4%
1 11888
39.6%

Most occurring scripts

ValueCountFrequency (%)
Common 30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 18112
60.4%
1 11888
39.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 18112
60.4%
1 11888
39.6%

Education
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8531333
Minimum0
Maximum6
Zeros14
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-05-17T04:02:10.195158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79034866
Coefficient of variation (CV)0.42649314
Kurtosis2.0786216
Mean1.8531333
Median Absolute Deviation (MAD)1
Skewness0.97097205
Sum55594
Variance0.624651
MonotonicityNot monotonic
2023-05-17T04:02:10.456858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 14030
46.8%
1 10585
35.3%
3 4917
 
16.4%
5 280
 
0.9%
4 123
 
0.4%
6 51
 
0.2%
0 14
 
< 0.1%
ValueCountFrequency (%)
0 14
 
< 0.1%
1 10585
35.3%
2 14030
46.8%
3 4917
 
16.4%
4 123
 
0.4%
5 280
 
0.9%
6 51
 
0.2%
ValueCountFrequency (%)
6 51
 
0.2%
5 280
 
0.9%
4 123
 
0.4%
3 4917
 
16.4%
2 14030
46.8%
1 10585
35.3%
0 14
 
< 0.1%

Marital_status
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
15964 
1
13659 
3
 
323
0
 
54

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 15964
53.2%
1 13659
45.5%
3 323
 
1.1%
0 54
 
0.2%

Length

2023-05-17T04:02:10.730567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-17T04:02:11.031460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 15964
53.2%
1 13659
45.5%
3 323
 
1.1%
0 54
 
0.2%

Most occurring characters

ValueCountFrequency (%)
2 15964
53.2%
1 13659
45.5%
3 323
 
1.1%
0 54
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 15964
53.2%
1 13659
45.5%
3 323
 
1.1%
0 54
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 15964
53.2%
1 13659
45.5%
3 323
 
1.1%
0 54
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 15964
53.2%
1 13659
45.5%
3 323
 
1.1%
0 54
 
0.2%

Age
Real number (ℝ)

Distinct56
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.4855
Minimum21
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-05-17T04:02:11.301961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median34
Q341
95-th percentile53
Maximum79
Range58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.2179041
Coefficient of variation (CV)0.25976537
Kurtosis0.044303378
Mean35.4855
Median Absolute Deviation (MAD)6
Skewness0.73224587
Sum1064565
Variance84.969755
MonotonicityNot monotonic
2023-05-17T04:02:11.626670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 1605
 
5.3%
27 1477
 
4.9%
28 1409
 
4.7%
30 1395
 
4.7%
26 1256
 
4.2%
31 1217
 
4.1%
25 1186
 
4.0%
34 1162
 
3.9%
32 1158
 
3.9%
33 1146
 
3.8%
Other values (46) 16989
56.6%
ValueCountFrequency (%)
21 67
 
0.2%
22 560
 
1.9%
23 931
3.1%
24 1127
3.8%
25 1186
4.0%
26 1256
4.2%
27 1477
4.9%
28 1409
4.7%
29 1605
5.3%
30 1395
4.7%
ValueCountFrequency (%)
79 1
 
< 0.1%
75 3
 
< 0.1%
74 1
 
< 0.1%
73 4
 
< 0.1%
72 3
 
< 0.1%
71 3
 
< 0.1%
70 10
< 0.1%
69 15
0.1%
68 5
 
< 0.1%
67 16
0.1%

Repayment_September
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0167
Minimum-2
Maximum8
Zeros14737
Zeros (%)49.1%
Negative8445
Negative (%)28.1%
Memory size1.5 MiB
2023-05-17T04:02:11.901091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1238015
Coefficient of variation (CV)-67.293505
Kurtosis2.720715
Mean-0.0167
Median Absolute Deviation (MAD)1
Skewness0.73197493
Sum-501
Variance1.2629299
MonotonicityNot monotonic
2023-05-17T04:02:12.158481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 14737
49.1%
-1 5686
 
19.0%
1 3688
 
12.3%
-2 2759
 
9.2%
2 2667
 
8.9%
3 322
 
1.1%
4 76
 
0.3%
5 26
 
0.1%
8 19
 
0.1%
6 11
 
< 0.1%
ValueCountFrequency (%)
-2 2759
 
9.2%
-1 5686
 
19.0%
0 14737
49.1%
1 3688
 
12.3%
2 2667
 
8.9%
3 322
 
1.1%
4 76
 
0.3%
5 26
 
0.1%
6 11
 
< 0.1%
7 9
 
< 0.1%
ValueCountFrequency (%)
8 19
 
0.1%
7 9
 
< 0.1%
6 11
 
< 0.1%
5 26
 
0.1%
4 76
 
0.3%
3 322
 
1.1%
2 2667
 
8.9%
1 3688
 
12.3%
0 14737
49.1%
-1 5686
 
19.0%

Repayment_August
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.13376667
Minimum-2
Maximum8
Zeros15730
Zeros (%)52.4%
Negative9832
Negative (%)32.8%
Memory size1.5 MiB
2023-05-17T04:02:12.431393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.197186
Coefficient of variation (CV)-8.9498079
Kurtosis1.5704177
Mean-0.13376667
Median Absolute Deviation (MAD)0
Skewness0.79056502
Sum-4013
Variance1.4332543
MonotonicityNot monotonic
2023-05-17T04:02:12.676346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 15730
52.4%
-1 6050
 
20.2%
2 3927
 
13.1%
-2 3782
 
12.6%
3 326
 
1.1%
4 99
 
0.3%
1 28
 
0.1%
5 25
 
0.1%
7 20
 
0.1%
6 12
 
< 0.1%
ValueCountFrequency (%)
-2 3782
 
12.6%
-1 6050
 
20.2%
0 15730
52.4%
1 28
 
0.1%
2 3927
 
13.1%
3 326
 
1.1%
4 99
 
0.3%
5 25
 
0.1%
6 12
 
< 0.1%
7 20
 
0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 20
 
0.1%
6 12
 
< 0.1%
5 25
 
0.1%
4 99
 
0.3%
3 326
 
1.1%
2 3927
 
13.1%
1 28
 
0.1%
0 15730
52.4%
-1 6050
 
20.2%

Repayment_July
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1662
Minimum-2
Maximum8
Zeros15764
Zeros (%)52.5%
Negative10023
Negative (%)33.4%
Memory size1.5 MiB
2023-05-17T04:02:12.911635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1968676
Coefficient of variation (CV)-7.2013692
Kurtosis2.0844359
Mean-0.1662
Median Absolute Deviation (MAD)0
Skewness0.84068183
Sum-4986
Variance1.432492
MonotonicityNot monotonic
2023-05-17T04:02:14.492720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 15764
52.5%
-1 5938
 
19.8%
-2 4085
 
13.6%
2 3819
 
12.7%
3 240
 
0.8%
4 76
 
0.3%
7 27
 
0.1%
6 23
 
0.1%
5 21
 
0.1%
1 4
 
< 0.1%
ValueCountFrequency (%)
-2 4085
 
13.6%
-1 5938
 
19.8%
0 15764
52.5%
1 4
 
< 0.1%
2 3819
 
12.7%
3 240
 
0.8%
4 76
 
0.3%
5 21
 
0.1%
6 23
 
0.1%
7 27
 
0.1%
ValueCountFrequency (%)
8 3
 
< 0.1%
7 27
 
0.1%
6 23
 
0.1%
5 21
 
0.1%
4 76
 
0.3%
3 240
 
0.8%
2 3819
 
12.7%
1 4
 
< 0.1%
0 15764
52.5%
-1 5938
 
19.8%

Repayment_June
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.22066667
Minimum-2
Maximum8
Zeros16455
Zeros (%)54.9%
Negative10035
Negative (%)33.5%
Memory size1.5 MiB
2023-05-17T04:02:14.725942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1691386
Coefficient of variation (CV)-5.2982113
Kurtosis3.4969835
Mean-0.22066667
Median Absolute Deviation (MAD)0
Skewness0.99962941
Sum-6620
Variance1.3668851
MonotonicityNot monotonic
2023-05-17T04:02:14.978065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 16455
54.9%
-1 5687
 
19.0%
-2 4348
 
14.5%
2 3159
 
10.5%
3 180
 
0.6%
4 69
 
0.2%
7 58
 
0.2%
5 35
 
0.1%
6 5
 
< 0.1%
1 2
 
< 0.1%
ValueCountFrequency (%)
-2 4348
 
14.5%
-1 5687
 
19.0%
0 16455
54.9%
1 2
 
< 0.1%
2 3159
 
10.5%
3 180
 
0.6%
4 69
 
0.2%
5 35
 
0.1%
6 5
 
< 0.1%
7 58
 
0.2%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 58
 
0.2%
6 5
 
< 0.1%
5 35
 
0.1%
4 69
 
0.2%
3 180
 
0.6%
2 3159
 
10.5%
1 2
 
< 0.1%
0 16455
54.9%
-1 5687
 
19.0%

Repayment_May
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2662
Minimum-2
Maximum8
Zeros16947
Zeros (%)56.5%
Negative10085
Negative (%)33.6%
Memory size1.5 MiB
2023-05-17T04:02:15.277755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1331874
Coefficient of variation (CV)-4.2569024
Kurtosis3.9897481
Mean-0.2662
Median Absolute Deviation (MAD)0
Skewness1.008197
Sum-7986
Variance1.2841137
MonotonicityNot monotonic
2023-05-17T04:02:15.651181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 16947
56.5%
-1 5539
 
18.5%
-2 4546
 
15.2%
2 2626
 
8.8%
3 178
 
0.6%
4 84
 
0.3%
7 58
 
0.2%
5 17
 
0.1%
6 4
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
-2 4546
 
15.2%
-1 5539
 
18.5%
0 16947
56.5%
2 2626
 
8.8%
3 178
 
0.6%
4 84
 
0.3%
5 17
 
0.1%
6 4
 
< 0.1%
7 58
 
0.2%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 58
 
0.2%
6 4
 
< 0.1%
5 17
 
0.1%
4 84
 
0.3%
3 178
 
0.6%
2 2626
 
8.8%
0 16947
56.5%
-1 5539
 
18.5%
-2 4546
 
15.2%

Repayment_April
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2911
Minimum-2
Maximum8
Zeros16286
Zeros (%)54.3%
Negative10635
Negative (%)35.4%
Memory size1.5 MiB
2023-05-17T04:02:16.130469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1499876
Coefficient of variation (CV)-3.95049
Kurtosis3.4265341
Mean-0.2911
Median Absolute Deviation (MAD)0
Skewness0.94802939
Sum-8733
Variance1.3224715
MonotonicityNot monotonic
2023-05-17T04:02:16.603688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 16286
54.3%
-1 5740
 
19.1%
-2 4895
 
16.3%
2 2766
 
9.2%
3 184
 
0.6%
4 49
 
0.2%
7 46
 
0.2%
6 19
 
0.1%
5 13
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
-2 4895
 
16.3%
-1 5740
 
19.1%
0 16286
54.3%
2 2766
 
9.2%
3 184
 
0.6%
4 49
 
0.2%
5 13
 
< 0.1%
6 19
 
0.1%
7 46
 
0.2%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 46
 
0.2%
6 19
 
0.1%
5 13
 
< 0.1%
4 49
 
0.2%
3 184
 
0.6%
2 2766
 
9.2%
0 16286
54.3%
-1 5740
 
19.1%
-2 4895
 
16.3%

Sep_Bill
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22723
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51223.331
Minimum-165580
Maximum964511
Zeros2008
Zeros (%)6.7%
Negative590
Negative (%)2.0%
Memory size1.5 MiB
2023-05-17T04:02:17.055365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-165580
5-th percentile0
Q13558.75
median22381.5
Q367091
95-th percentile201203.05
Maximum964511
Range1130091
Interquartile range (IQR)63532.25

Descriptive statistics

Standard deviation73635.861
Coefficient of variation (CV)1.4375453
Kurtosis9.8062893
Mean51223.331
Median Absolute Deviation (MAD)21800.5
Skewness2.663861
Sum1.5366999 × 109
Variance5.42224 × 109
MonotonicityNot monotonic
2023-05-17T04:02:17.608152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2008
 
6.7%
390 244
 
0.8%
780 76
 
0.3%
326 72
 
0.2%
316 63
 
0.2%
2500 59
 
0.2%
396 49
 
0.2%
2400 39
 
0.1%
416 29
 
0.1%
500 25
 
0.1%
Other values (22713) 27336
91.1%
ValueCountFrequency (%)
-165580 1
< 0.1%
-154973 1
< 0.1%
-15308 1
< 0.1%
-14386 1
< 0.1%
-11545 1
< 0.1%
-10682 1
< 0.1%
-9802 1
< 0.1%
-9095 1
< 0.1%
-8187 1
< 0.1%
-7438 1
< 0.1%
ValueCountFrequency (%)
964511 1
< 0.1%
746814 1
< 0.1%
653062 1
< 0.1%
630458 1
< 0.1%
626648 1
< 0.1%
621749 1
< 0.1%
613860 1
< 0.1%
610723 1
< 0.1%
608594 1
< 0.1%
604019 1
< 0.1%

Aug_Bill
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22346
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49179.075
Minimum-69777
Maximum983931
Zeros2506
Zeros (%)8.4%
Negative669
Negative (%)2.2%
Memory size1.5 MiB
2023-05-17T04:02:18.200701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-69777
5-th percentile0
Q12984.75
median21200
Q364006.25
95-th percentile194792.2
Maximum983931
Range1053708
Interquartile range (IQR)61021.5

Descriptive statistics

Standard deviation71173.769
Coefficient of variation (CV)1.4472368
Kurtosis10.302946
Mean49179.075
Median Absolute Deviation (MAD)20810
Skewness2.7052209
Sum1.4753723 × 109
Variance5.0657054 × 109
MonotonicityNot monotonic
2023-05-17T04:02:18.812797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2506
 
8.4%
390 231
 
0.8%
326 75
 
0.2%
780 75
 
0.2%
316 72
 
0.2%
396 51
 
0.2%
2500 51
 
0.2%
2400 42
 
0.1%
-200 29
 
0.1%
416 28
 
0.1%
Other values (22336) 26840
89.5%
ValueCountFrequency (%)
-69777 1
< 0.1%
-67526 1
< 0.1%
-33350 1
< 0.1%
-30000 1
< 0.1%
-26214 1
< 0.1%
-24704 1
< 0.1%
-24702 1
< 0.1%
-22960 1
< 0.1%
-18618 1
< 0.1%
-18088 1
< 0.1%
ValueCountFrequency (%)
983931 1
< 0.1%
743970 1
< 0.1%
671563 1
< 0.1%
646770 1
< 0.1%
624475 1
< 0.1%
605943 1
< 0.1%
597793 1
< 0.1%
586825 1
< 0.1%
581775 1
< 0.1%
577681 1
< 0.1%

July_Bill
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22026
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47013.155
Minimum-157264
Maximum1664089
Zeros2870
Zeros (%)9.6%
Negative655
Negative (%)2.2%
Memory size1.5 MiB
2023-05-17T04:02:19.374642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-157264
5-th percentile0
Q12666.25
median20088.5
Q360164.75
95-th percentile187821.05
Maximum1664089
Range1821353
Interquartile range (IQR)57498.5

Descriptive statistics

Standard deviation69349.387
Coefficient of variation (CV)1.475106
Kurtosis19.783255
Mean47013.155
Median Absolute Deviation (MAD)19708.5
Skewness3.08783
Sum1.4103946 × 109
Variance4.8093375 × 109
MonotonicityNot monotonic
2023-05-17T04:02:19.912138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2870
 
9.6%
390 275
 
0.9%
780 74
 
0.2%
326 63
 
0.2%
316 62
 
0.2%
396 48
 
0.2%
2500 40
 
0.1%
2400 39
 
0.1%
416 29
 
0.1%
200 27
 
0.1%
Other values (22016) 26473
88.2%
ValueCountFrequency (%)
-157264 1
< 0.1%
-61506 1
< 0.1%
-46127 1
< 0.1%
-34041 1
< 0.1%
-25443 1
< 0.1%
-24702 1
< 0.1%
-20320 1
< 0.1%
-17706 1
< 0.1%
-15910 1
< 0.1%
-15641 1
< 0.1%
ValueCountFrequency (%)
1664089 1
< 0.1%
855086 1
< 0.1%
693131 1
< 0.1%
689643 1
< 0.1%
689627 1
< 0.1%
632041 1
< 0.1%
597415 1
< 0.1%
578971 1
< 0.1%
577957 1
< 0.1%
577015 1
< 0.1%

June_Bill
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21548
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43262.949
Minimum-170000
Maximum891586
Zeros3195
Zeros (%)10.7%
Negative675
Negative (%)2.2%
Memory size1.5 MiB
2023-05-17T04:02:20.232325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-170000
5-th percentile0
Q12326.75
median19052
Q354506
95-th percentile174333.35
Maximum891586
Range1061586
Interquartile range (IQR)52179.25

Descriptive statistics

Standard deviation64332.856
Coefficient of variation (CV)1.4870197
Kurtosis11.309325
Mean43262.949
Median Absolute Deviation (MAD)18656
Skewness2.8219653
Sum1.2978885 × 109
Variance4.1387164 × 109
MonotonicityNot monotonic
2023-05-17T04:02:20.557377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3195
 
10.7%
390 246
 
0.8%
780 101
 
0.3%
316 68
 
0.2%
326 62
 
0.2%
396 44
 
0.1%
2400 39
 
0.1%
150 39
 
0.1%
2500 34
 
0.1%
416 33
 
0.1%
Other values (21538) 26139
87.1%
ValueCountFrequency (%)
-170000 1
< 0.1%
-81334 1
< 0.1%
-65167 1
< 0.1%
-50616 1
< 0.1%
-46627 1
< 0.1%
-34503 1
< 0.1%
-27490 1
< 0.1%
-24303 1
< 0.1%
-22108 1
< 0.1%
-20320 1
< 0.1%
ValueCountFrequency (%)
891586 1
< 0.1%
706864 1
< 0.1%
628699 1
< 0.1%
616836 1
< 0.1%
572805 1
< 0.1%
569034 1
< 0.1%
565669 1
< 0.1%
563543 1
< 0.1%
548020 1
< 0.1%
542653 1
< 0.1%

May_Bill
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21010
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40311.401
Minimum-81334
Maximum927171
Zeros3506
Zeros (%)11.7%
Negative655
Negative (%)2.2%
Memory size1.5 MiB
2023-05-17T04:02:20.888262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-81334
5-th percentile0
Q11763
median18104.5
Q350190.5
95-th percentile165794.3
Maximum927171
Range1008505
Interquartile range (IQR)48427.5

Descriptive statistics

Standard deviation60797.156
Coefficient of variation (CV)1.5081876
Kurtosis12.305881
Mean40311.401
Median Absolute Deviation (MAD)17688.5
Skewness2.8763799
Sum1.209342 × 109
Variance3.6962941 × 109
MonotonicityNot monotonic
2023-05-17T04:02:21.192426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3506
 
11.7%
390 235
 
0.8%
780 94
 
0.3%
316 79
 
0.3%
326 62
 
0.2%
150 58
 
0.2%
396 47
 
0.2%
2400 39
 
0.1%
2500 37
 
0.1%
416 36
 
0.1%
Other values (21000) 25807
86.0%
ValueCountFrequency (%)
-81334 1
< 0.1%
-61372 1
< 0.1%
-53007 1
< 0.1%
-46627 1
< 0.1%
-37594 1
< 0.1%
-36156 1
< 0.1%
-30481 1
< 0.1%
-28335 1
< 0.1%
-23003 1
< 0.1%
-20753 1
< 0.1%
ValueCountFrequency (%)
927171 1
< 0.1%
823540 1
< 0.1%
587067 1
< 0.1%
551702 1
< 0.1%
547880 1
< 0.1%
530672 1
< 0.1%
524315 1
< 0.1%
516139 1
< 0.1%
514114 1
< 0.1%
508213 1
< 0.1%

Apr_Bill
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20604
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38871.76
Minimum-339603
Maximum961664
Zeros4020
Zeros (%)13.4%
Negative688
Negative (%)2.3%
Memory size1.5 MiB
2023-05-17T04:02:21.517390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-339603
5-th percentile0
Q11256
median17071
Q349198.25
95-th percentile161912
Maximum961664
Range1301267
Interquartile range (IQR)47942.25

Descriptive statistics

Standard deviation59554.108
Coefficient of variation (CV)1.5320661
Kurtosis12.270705
Mean38871.76
Median Absolute Deviation (MAD)16755
Skewness2.8466446
Sum1.1661528 × 109
Variance3.5466917 × 109
MonotonicityNot monotonic
2023-05-17T04:02:21.852735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4020
 
13.4%
390 207
 
0.7%
780 86
 
0.3%
150 78
 
0.3%
316 77
 
0.3%
326 56
 
0.2%
396 45
 
0.1%
416 36
 
0.1%
-18 33
 
0.1%
2400 32
 
0.1%
Other values (20594) 25330
84.4%
ValueCountFrequency (%)
-339603 1
< 0.1%
-209051 1
< 0.1%
-150953 1
< 0.1%
-94625 1
< 0.1%
-73895 1
< 0.1%
-57060 1
< 0.1%
-51443 1
< 0.1%
-51183 1
< 0.1%
-46627 1
< 0.1%
-45734 1
< 0.1%
ValueCountFrequency (%)
961664 1
< 0.1%
699944 1
< 0.1%
568638 1
< 0.1%
527711 1
< 0.1%
527566 1
< 0.1%
514975 1
< 0.1%
513798 1
< 0.1%
511905 1
< 0.1%
501370 1
< 0.1%
499100 1
< 0.1%

Pay_Sep
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7943
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5663.5805
Minimum0
Maximum873552
Zeros5249
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-05-17T04:02:22.169888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median2100
Q35006
95-th percentile18428.2
Maximum873552
Range873552
Interquartile range (IQR)4006

Descriptive statistics

Standard deviation16563.28
Coefficient of variation (CV)2.9245246
Kurtosis415.25474
Mean5663.5805
Median Absolute Deviation (MAD)1932
Skewness14.668364
Sum1.6990742 × 108
Variance2.7434226 × 108
MonotonicityNot monotonic
2023-05-17T04:02:22.472147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5249
 
17.5%
2000 1363
 
4.5%
3000 891
 
3.0%
5000 698
 
2.3%
1500 507
 
1.7%
4000 426
 
1.4%
10000 401
 
1.3%
1000 365
 
1.2%
2500 298
 
1.0%
6000 294
 
1.0%
Other values (7933) 19508
65.0%
ValueCountFrequency (%)
0 5249
17.5%
1 9
 
< 0.1%
2 14
 
< 0.1%
3 15
 
0.1%
4 18
 
0.1%
5 12
 
< 0.1%
6 15
 
0.1%
7 9
 
< 0.1%
8 8
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
873552 1
< 0.1%
505000 1
< 0.1%
493358 1
< 0.1%
423903 1
< 0.1%
405016 1
< 0.1%
368199 1
< 0.1%
323014 1
< 0.1%
304815 1
< 0.1%
302000 1
< 0.1%
300039 1
< 0.1%

Pay_Aug
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct7899
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5921.1635
Minimum0
Maximum1684259
Zeros5396
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-05-17T04:02:22.778960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1833
median2009
Q35000
95-th percentile19004.35
Maximum1684259
Range1684259
Interquartile range (IQR)4167

Descriptive statistics

Standard deviation23040.87
Coefficient of variation (CV)3.8912741
Kurtosis1641.6319
Mean5921.1635
Median Absolute Deviation (MAD)1991
Skewness30.453817
Sum1.776349 × 108
Variance5.3088171 × 108
MonotonicityNot monotonic
2023-05-17T04:02:23.101565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5396
 
18.0%
2000 1290
 
4.3%
3000 857
 
2.9%
5000 717
 
2.4%
1000 594
 
2.0%
1500 521
 
1.7%
4000 410
 
1.4%
10000 318
 
1.1%
6000 283
 
0.9%
2500 251
 
0.8%
Other values (7889) 19363
64.5%
ValueCountFrequency (%)
0 5396
18.0%
1 15
 
0.1%
2 20
 
0.1%
3 18
 
0.1%
4 11
 
< 0.1%
5 25
 
0.1%
6 8
 
< 0.1%
7 12
 
< 0.1%
8 9
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
1684259 1
< 0.1%
1227082 1
< 0.1%
1215471 1
< 0.1%
1024516 1
< 0.1%
580464 1
< 0.1%
415552 1
< 0.1%
401003 1
< 0.1%
388126 1
< 0.1%
385228 1
< 0.1%
384986 1
< 0.1%

Pay_July
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7518
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5225.6815
Minimum0
Maximum896040
Zeros5968
Zeros (%)19.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-05-17T04:02:23.438508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1390
median1800
Q34505
95-th percentile17589.4
Maximum896040
Range896040
Interquartile range (IQR)4115

Descriptive statistics

Standard deviation17606.961
Coefficient of variation (CV)3.3693139
Kurtosis564.31123
Mean5225.6815
Median Absolute Deviation (MAD)1795
Skewness17.216635
Sum1.5677044 × 108
Variance3.1000509 × 108
MonotonicityNot monotonic
2023-05-17T04:02:23.760681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5968
 
19.9%
2000 1285
 
4.3%
1000 1103
 
3.7%
3000 870
 
2.9%
5000 721
 
2.4%
1500 490
 
1.6%
4000 381
 
1.3%
10000 312
 
1.0%
1200 243
 
0.8%
6000 241
 
0.8%
Other values (7508) 18386
61.3%
ValueCountFrequency (%)
0 5968
19.9%
1 13
 
< 0.1%
2 19
 
0.1%
3 14
 
< 0.1%
4 15
 
0.1%
5 18
 
0.1%
6 14
 
< 0.1%
7 18
 
0.1%
8 10
 
< 0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
896040 1
< 0.1%
889043 1
< 0.1%
508229 1
< 0.1%
417588 1
< 0.1%
400972 1
< 0.1%
397092 1
< 0.1%
380478 1
< 0.1%
371718 1
< 0.1%
349395 1
< 0.1%
344261 1
< 0.1%

Pay_June
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6937
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4826.0769
Minimum0
Maximum621000
Zeros6408
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-05-17T04:02:24.060004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1296
median1500
Q34013.25
95-th percentile16014.95
Maximum621000
Range621000
Interquartile range (IQR)3717.25

Descriptive statistics

Standard deviation15666.16
Coefficient of variation (CV)3.246148
Kurtosis277.33377
Mean4826.0769
Median Absolute Deviation (MAD)1500
Skewness12.904985
Sum1.4478231 × 108
Variance2.4542856 × 108
MonotonicityNot monotonic
2023-05-17T04:02:24.370494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6408
 
21.4%
1000 1394
 
4.6%
2000 1214
 
4.0%
3000 887
 
3.0%
5000 810
 
2.7%
1500 441
 
1.5%
4000 402
 
1.3%
10000 341
 
1.1%
2500 259
 
0.9%
500 258
 
0.9%
Other values (6927) 17586
58.6%
ValueCountFrequency (%)
0 6408
21.4%
1 22
 
0.1%
2 22
 
0.1%
3 13
 
< 0.1%
4 20
 
0.1%
5 12
 
< 0.1%
6 16
 
0.1%
7 11
 
< 0.1%
8 7
 
< 0.1%
9 9
 
< 0.1%
ValueCountFrequency (%)
621000 1
< 0.1%
528897 1
< 0.1%
497000 1
< 0.1%
432130 1
< 0.1%
400046 1
< 0.1%
331788 1
< 0.1%
330982 1
< 0.1%
320008 1
< 0.1%
313094 1
< 0.1%
292962 1
< 0.1%

Pay_May
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6897
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4799.3876
Minimum0
Maximum426529
Zeros6703
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-05-17T04:02:24.692705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1252.5
median1500
Q34031.5
95-th percentile16000
Maximum426529
Range426529
Interquartile range (IQR)3779

Descriptive statistics

Standard deviation15278.306
Coefficient of variation (CV)3.1833865
Kurtosis180.06394
Mean4799.3876
Median Absolute Deviation (MAD)1500
Skewness11.127417
Sum1.4398163 × 108
Variance2.3342662 × 108
MonotonicityNot monotonic
2023-05-17T04:02:25.013596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6703
 
22.3%
1000 1340
 
4.5%
2000 1323
 
4.4%
3000 947
 
3.2%
5000 814
 
2.7%
1500 426
 
1.4%
4000 401
 
1.3%
10000 343
 
1.1%
500 250
 
0.8%
6000 247
 
0.8%
Other values (6887) 17206
57.4%
ValueCountFrequency (%)
0 6703
22.3%
1 21
 
0.1%
2 13
 
< 0.1%
3 13
 
< 0.1%
4 12
 
< 0.1%
5 9
 
< 0.1%
6 7
 
< 0.1%
7 9
 
< 0.1%
8 6
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
426529 1
< 0.1%
417990 1
< 0.1%
388071 1
< 0.1%
379267 1
< 0.1%
332000 1
< 0.1%
331788 1
< 0.1%
330982 1
< 0.1%
326889 1
< 0.1%
317077 1
< 0.1%
310135 1
< 0.1%

Pay_April
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6939
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5215.5026
Minimum0
Maximum528666
Zeros7173
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-05-17T04:02:25.300683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1117.75
median1500
Q34000
95-th percentile17343.8
Maximum528666
Range528666
Interquartile range (IQR)3882.25

Descriptive statistics

Standard deviation17777.466
Coefficient of variation (CV)3.4085815
Kurtosis167.16143
Mean5215.5026
Median Absolute Deviation (MAD)1500
Skewness10.640727
Sum1.5646508 × 108
Variance3.1603829 × 108
MonotonicityNot monotonic
2023-05-17T04:02:25.625000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7173
23.9%
1000 1299
 
4.3%
2000 1295
 
4.3%
3000 914
 
3.0%
5000 808
 
2.7%
1500 439
 
1.5%
4000 411
 
1.4%
10000 356
 
1.2%
500 247
 
0.8%
6000 220
 
0.7%
Other values (6929) 16838
56.1%
ValueCountFrequency (%)
0 7173
23.9%
1 20
 
0.1%
2 9
 
< 0.1%
3 14
 
< 0.1%
4 12
 
< 0.1%
5 7
 
< 0.1%
6 6
 
< 0.1%
7 5
 
< 0.1%
8 6
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
528666 1
< 0.1%
527143 1
< 0.1%
443001 1
< 0.1%
422000 1
< 0.1%
403500 1
< 0.1%
377000 1
< 0.1%
372495 1
< 0.1%
351282 1
< 0.1%
345293 1
< 0.1%
308000 1
< 0.1%

Defaulter
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
23364 
1
6636 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Length

2023-05-17T04:02:25.914645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-17T04:02:26.183795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring characters

ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
Common 30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Interactions

2023-05-17T04:02:00.305749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:42.141834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:49.429229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:57.759887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:06.875365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:13.538579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:23.027925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:30.840249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:37.971270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:43.515633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:50.782141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:57.164525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:03.844960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:09.950939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:15.471564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:22.431801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:28.061486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:36.192037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:41.785304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:48.668952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:54.779955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:02:00.660072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:42.392757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:49.795547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:58.124594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:07.140659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:13.854169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:23.305600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:31.262616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:38.234517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:43.777916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:51.029717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:57.424031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:04.297239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:10.201876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:15.897194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:22.696271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:28.311019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:36.476095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:42.037296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:49.114013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:55.036231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:02:01.022314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:42.661899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:50.070838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:58.463382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:07.409628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:14.176621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:23.628850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:31.670627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:38.516804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:44.047136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:51.293661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:57.681069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:04.669691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:10.491109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:16.295522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:22.971447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:29.563647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:36.735339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:42.294961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:49.575310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:55.285959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:02:01.397591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:42.910166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:51.382341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:58.737103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:07.719470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:14.440991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:23.903200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:32.076657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:38.783907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:44.308285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:51.564475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:57.940050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:05.063158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:10.753151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:16.681565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:23.235149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:29.824860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:36.994888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:42.553536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:49.996560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:55.539927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:02:01.790423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:43.160129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:51.643943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:59.073547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:08.037189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:14.799370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:24.272495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:32.474811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:39.050376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:44.584215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:51.826271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:58.206481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:05.348773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:11.005784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:17.013878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:23.504093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:30.135412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:37.255860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:42.794838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:50.269014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:55.805562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:02:02.178172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:43.409487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:51.918704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:59.381356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:08.407519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:15.173821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:24.627410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:32.887512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:39.303517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:44.834600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:52.079906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:58.447326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:05.614836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:11.253197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:17.372725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:23.768734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:30.494020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:37.513212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:43.034862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:50.535546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:56.059832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:02:02.525588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:43.661625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:52.341554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:59.663235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:08.727475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:15.725381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:24.938190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:33.284909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:39.572160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:45.084227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:52.337076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:58.700938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:05.887013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:11.519861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:17.785263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:24.049935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:30.857508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:37.777731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:43.279626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-05-17T04:00:55.819019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:01.771192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:08.610559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:14.025251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:21.185474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:26.722816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:34.879786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:40.441530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:46.255831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:53.495502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:58.896589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:02:06.451324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:47.492314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:56.440543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:05.515992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:12.211040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:21.032724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:29.702419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:36.948123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:42.444786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:49.466897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:56.110870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:02.215252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:08.894112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:14.299865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:21.445232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:27.014992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:35.165480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:40.728961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:46.654250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:53.767819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:59.180366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:02:06.701147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:47.927483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:56.785522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:05.897675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:12.546756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:22.206804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:29.968202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:37.189045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:42.713661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:49.906278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:56.365237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:02.586003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:09.151559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:14.569585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:21.682133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:27.268611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:35.417964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:40.990940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:47.552180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:54.016194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:59.430093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:02:06.969853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:48.338261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:57.124126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:06.213696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:12.860976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:22.484123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:30.239496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:37.466815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:42.997644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:50.271723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:56.644753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:03.010047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:09.427443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:14.848599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:21.949824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:27.537846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:35.696082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:41.255747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:47.965339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:54.275616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:59.698725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:02:07.219306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:48.994589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T03:59:57.484965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:06.492349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:13.107286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:22.736349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:30.517490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:37.719751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:43.249092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:50.510055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:00:56.897577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:03.438320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:09.684796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:15.096327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:22.181948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:27.788343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:35.947153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:41.522039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:48.318739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:54.517551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-17T04:01:59.945601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-05-17T04:02:26.437696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Limit_balEducationAgeRepayment_SeptemberRepayment_AugustRepayment_JulyRepayment_JuneRepayment_MayRepayment_AprilSep_BillAug_BillJuly_BillJune_BillMay_BillApr_BillPay_SepPay_AugPay_JulyPay_JunePay_MayPay_AprilGenderMarital_statusDefaulter
Limit_bal1.000-0.2640.186-0.296-0.343-0.332-0.309-0.285-0.2640.0540.0490.0610.0730.0810.0880.2720.2780.2840.2830.2940.3170.0730.0640.157
Education-0.2641.0000.1590.1320.1690.1620.1520.1370.1240.0950.0920.0810.0690.0600.056-0.042-0.047-0.042-0.043-0.049-0.0530.0290.1140.072
Age0.1860.1591.000-0.064-0.083-0.083-0.080-0.083-0.0760.0010.0020.002-0.003-0.0000.0000.0340.0440.0330.0410.0380.0390.0910.2860.048
Repayment_September-0.2960.132-0.0641.0000.6270.5480.5160.4860.4640.3150.3300.3140.3070.2990.289-0.098-0.064-0.054-0.034-0.026-0.0450.0590.0360.422
Repayment_August-0.3430.169-0.0830.6271.0000.7990.7130.6740.6350.5710.5510.5190.4980.4780.4590.0200.0840.0870.0950.0990.0820.0710.0340.340
Repayment_July-0.3320.162-0.0830.5480.7991.0000.8010.7180.6710.5240.5890.5570.5310.5070.4850.2160.0370.1030.1190.1240.0980.0670.0320.294
Repayment_June-0.3090.152-0.0800.5160.7130.8011.0000.8220.7320.5120.5580.6190.5930.5610.5340.1850.2460.0690.1440.1620.1430.0630.0350.278
Repayment_May-0.2850.137-0.0830.4860.6740.7180.8221.0000.8210.4990.5380.5870.6500.6180.5790.1750.2220.2600.1070.1850.1720.0560.0330.269
Repayment_April-0.2640.124-0.0760.4640.6350.6710.7320.8211.0000.4880.5240.5610.6060.6680.6300.1780.2000.2380.2840.1410.1980.0470.0300.249
Sep_Bill0.0540.0950.0010.3150.5710.5240.5120.4990.4881.0000.9110.8580.8070.7690.7340.5020.4720.4410.4420.4250.4100.0260.0170.031
Aug_Bill0.0490.0920.0020.3300.5510.5890.5580.5380.5240.9111.0000.9080.8480.8030.7650.6360.4980.4680.4610.4490.4290.0330.0120.031
July_Bill0.0610.0810.0020.3140.5190.5570.6190.5870.5610.8580.9081.0000.9040.8490.8040.5500.6380.4920.4890.4770.4580.0180.0120.000
June_Bill0.0730.069-0.0030.3070.4980.5310.5930.6500.6060.8070.8480.9041.0000.9030.8480.5120.5550.6340.5070.5040.4810.0260.0140.019
May_Bill0.0810.060-0.0000.2990.4780.5070.5610.6180.6680.7690.8030.8490.9031.0000.9020.4830.5150.5490.6470.5250.5090.0210.0150.017
Apr_Bill0.0880.0560.0000.2890.4590.4850.5340.5790.6300.7340.7650.8040.8480.9021.0000.4560.4870.5190.5700.6660.5290.0260.0160.022
Pay_Sep0.272-0.0420.034-0.0980.0200.2160.1850.1750.1780.5020.6360.5500.5120.4830.4561.0000.5120.5190.4860.4680.4550.0000.0310.027
Pay_Aug0.278-0.0470.044-0.0640.0840.0370.2460.2220.2000.4720.4980.6380.5550.5150.4870.5121.0000.5160.5200.4970.4910.0000.0200.013
Pay_July0.284-0.0420.033-0.0540.0870.1030.0690.2600.2380.4410.4680.4920.6340.5490.5190.5190.5161.0000.5160.5340.5050.0120.0190.024
Pay_June0.283-0.0430.041-0.0340.0950.1190.1440.1070.2840.4420.4610.4890.5070.6470.5700.4860.5200.5161.0000.5340.5470.0000.0300.022
Pay_May0.294-0.0490.038-0.0260.0990.1240.1620.1850.1410.4250.4490.4770.5040.5250.6660.4680.4970.5340.5341.0000.5490.0140.0000.035
Pay_April0.317-0.0530.039-0.0450.0820.0980.1430.1720.1980.4100.4290.4580.4810.5090.5290.4550.4910.5050.5470.5491.0000.0120.0000.028
Gender0.0730.0290.0910.0590.0710.0670.0630.0560.0470.0260.0330.0180.0260.0210.0260.0000.0000.0120.0000.0140.0121.0000.0320.039
Marital_status0.0640.1140.2860.0360.0340.0320.0350.0330.0300.0170.0120.0120.0140.0150.0160.0310.0200.0190.0300.0000.0000.0321.0000.033
Defaulter0.1570.0720.0480.4220.3400.2940.2780.2690.2490.0310.0310.0000.0190.0170.0220.0270.0130.0240.0220.0350.0280.0390.0331.000

Missing values

2023-05-17T04:02:07.641898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-17T04:02:08.469090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Limit_balGenderEducationMarital_statusAgeRepayment_SeptemberRepayment_AugustRepayment_JulyRepayment_JuneRepayment_MayRepayment_AprilSep_BillAug_BillJuly_BillJune_BillMay_BillApr_BillPay_SepPay_AugPay_JulyPay_JunePay_MayPay_AprilDefaulter
ID
1200002212422-1-1-2-239133102689000068900001
212000022226-1200022682172526823272345532610100010001000020001
390000222340000002923914027135591433114948155491518150010001000100050000
450000221370000004699048233492912831428959295472000201912001100106910000
55000012157-10-100086175670358352094019146191312000366811000090006896790
6500001123700000064400570695760819394196192002425001815657100010008000
7500000112290000003679654120234450075426534830034739445500040000380002023913750137700
8100000222230-1-100-111876380601221-1595673806010581168715420
91400002312800200011285140961210812211117933719332904321000100010000
102000013235-2-2-2-2-1-10000130071391200013007112200
Limit_balGenderEducationMarital_statusAgeRepayment_SeptemberRepayment_AugustRepayment_JulyRepayment_JuneRepayment_MayRepayment_AprilSep_BillAug_BillJuly_BillJune_BillMay_BillApr_BillPay_SepPay_AugPay_JulyPay_JunePay_MayPay_AprilDefaulter
ID
299911400001214100000013832513714213911013826249675461216000700042281505200020000
29992210000121343222222500250025002500250025000000001
299931000013143000-2-2-288021040000002000000000
29994100000112380-1-100030421427102996706266947355004200011178440003000200020000
2999580000122342222227255777708793847751982607811587000350007000040001
299962200001313900000018894819281520836588004312371598085002000050033047500010000
2999715000013243-1-1-1-100168318283502897951900183735268998129000
299983000012237432-10035653356275820878205821935700220004200200031001
2999980000131411-1000-1-16457837976304527741185548944859003409117819265296418041
3000050000121460000004792948905497643653532428153132078180014301000100010001

Duplicate rows

Most frequently occurring

Limit_balGenderEducationMarital_statusAgeRepayment_SeptemberRepayment_AugustRepayment_JulyRepayment_JuneRepayment_MayRepayment_AprilSep_BillAug_BillJuly_BillJune_BillMay_BillApr_BillPay_SepPay_AugPay_JulyPay_JunePay_MayPay_AprilDefaulter# duplicates
0200001222422444416501650165016501650165000000012
150000122261-2-2-2-2-200000000000002
250000212231-2-2-2-2-200000000000002
38000022131-2-2-2-2-2-200000000000002
48000022225-2-2-2-2-2-200000000000002
58000023142-2-2-2-2-2-200000000000002
690000212311-2-2-2-2-200000000000002
7100000221491-2-2-2-2-200000000000002
8110000212311-2-2-2-2-200000000000002
9140000112291-2-2-2-2-200000000000002